{
 "metadata": {
  "description": "Play with the trained speech recognition model from words_net.prototxt",
  "example_name": "Editing model parameters",
  "include_in_docs": true,
  "name": "",
  "priority": 5,
  "signature": "sha256:dad712d139bf18e47deac2283acc0df43562ce7bd5fb525429039533f5838e02"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Play with the trained speech recognition model from words_net.prototxt: numbers_iter_5000.caffemodel\n",
      "## Notebook might need minor adjustments on your machine\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make sure that caffe is on the python path:\n",
      "# this file is expected to be in {caffe_root}/examples/speech\n",
      "\n",
      "caffe_root = '~/caffe/' \n",
      "import sys\n",
      "sys.path.insert(0, caffe_root + 'python')\n",
      "sys.path.insert(0, caffe_root + 'python/caffe')\n",
      "import caffe\n",
      "\n",
      "model=\"numbers_deploy.prototxt\"\n",
      "weights=\"numbers_iter_5000.caffemodel\"\n",
      "net = caffe.Net(model,weights)\n",
      "net.set_phase_test()\n",
      "net.set_raw_scale('data', 255.0)\n",
      "net.set_mode_gpu()\n",
      "\n",
      "# Prime a second net with some numbers weights\n",
      "# model2=caffe_root+\"/examples/speech/numbers_net.prototxt30\"\n",
      "# copy_net = caffe.Net(model2)\n",
      "# net.share_with(copy_net)\n",
      "# copy_net.save(caffe_root+\"test.net\") YAY! OK!!\n",
      "\n",
      "# some reflections:\n",
      "# help(net)\n",
      "# print net.params\n",
      "# print net.layers #LayerVec\n",
      "# print net.layers[0] #PyLayer __reduce__() .blobs\n",
      "# c++: layer_param,loss(int top_index),type_name(),\n",
      "# print net.layers[0].blobs[0] #Blob \n",
      "# Blob   channels, count,data,diff,height,num,width\n",
      "# print net.layers[0].type_name #added in branch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#import Image\n",
      "import numpy as np\n",
      "from PIL import Image as Image\n",
      "import matplotlib.pyplot as pyplot\n",
      "import skimage.io\n",
      "\n",
      "# load input and configure preprocessing\n",
      "image_file=caffe_root+\"/examples/speech/spoken_numbers/7_Karen_260.wav.png\"\n",
      "# input_image = skimage.io.imread(image_file).astype(np.float32)\n",
      "input_image = caffe.iox.load_image(image_file)\n",
      "plt.imshow(input_image)\n",
      "print im.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "# make classification map by forward and print prediction indices at each location\n",
      "data=np.asarray([net.preprocess('data', im)])\n",
      "out = net.forward_all(data=data)\n",
      "print out.keys()\n",
      "print out['prob'][0]\n",
      "# print out['prob'][0].argmax(axis=0)\n",
      "\n",
      "# show net input and confidence map (probability of the top prediction at each location)\n",
      "plt.subplot(1, 2, 1)\n",
      "plt.imshow(out['prob'][0].max(axis=0))\n",
      "plt.subplot(1, 2, 2)\n",
      "data2=data #net.blobs['data'].data[0] #huh?\n",
      "plt.imshow(net.deprocess('data', data2))\n",
      "#print out => 7 YAY !!\n",
      "# {'prob': array([[[[ 0.]],[[ 0.]],[[ 0.]],[[ 0.]],[[ 0.]],[[ 0.]],[[ 0.]],[[ 1.]],[[ 0.]],[[ 0.]]]], dtype=float32)}\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['prob']\n",
        "[[[ 0.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 1.]]\n",
        "\n",
        " [[ 0.]]\n",
        "\n",
        " [[ 0.]]]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f60b097d190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lfD6fyrxLpZLqxpUMWnYB+rxb8eCRoqrINwGWlpZU5i6KGpF4SgFZagRS+BXf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\nm29y++23A3DhwgVgoLPX0O8stkXLIdx///2D9hNN2wXRTh8Gh6J0Ot0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       "text": [
        "<matplotlib.figure.Figure at 0x7f60b0ad2b90>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}